This document discusses future innovation in stroke prevention. It notes that atrial fibrillation (AF) is a major cause of strokes, yet many cases go undetected and untreated. New technologies like wearables can now detect AF with 97% accuracy. The Apple Heart Study is looking to get FDA approval for the Apple Watch as a medical device for AF detection. This could lead to many "expert patients" who self-diagnose AF. Hospitals will need strategies for how to manage these patients. The document advocates for improved integration of health information to better detect, review, and treat AF to prevent strokes. Initial steps proposed include using local cost data to prioritize efforts, setting up an AF detection strategy, and considering information integration through

1. Future Innovation in Stroke
Prevention
Dr. Andrew Hill
Clinical Lead for Stroke Services,
St Helens and Knowsley Teaching Hospitals NHS Trust
Director of Clinical Informatics,
King’s College London / Royal College of Physicians Stroke Sentinel
National Audit Programme
Email: Andrew.hill@doctors.org.uk
Twitter: @drewhill79 @sthknhs

2. Conficts of interest
• No financial interests from third parties involved in the development
or treatment of atrial fibrillation.
• AH is a (non-paid) contributing author to ResearchKit, an open source
medical research programming library used on Apple devices. This
library has been used to write the software driving the Apple Health
study (mentioned later in talk).

3. Context
• Stroke is the endpoint measure for failed atrial fibrillation prevention,
detection and treatment strategies.
• AF causes the largest strokes:
• Mortality for stroke with AF is 33%
• Stroke without AF is around 10%.
• Anticoagulation reduces risk of stroke by 2/3 (ie. ‘on balance of
probabilities the stroke will not occur).
• The incidence and quality of management of AF in a region is likely to
be a big predictor of casemix and hence crude mortality on a stroke
unit.
• Additionally, micro-infarcts from chronic undetected and/or untrated
AF likely to be a big contributor to vascular dementia in populations.

4. Measuring AF Detection and Treatment Outcomes From Stroke Care
• Stroke Sentinel National Audit Data measures:
• Whether patients have new, or known AF.
• Whether the patient as on antiplatelets or anticoagulants pre-stroke.
• Stroke severity and impact.
• Outcome and discharge and death.
• Most Trusts engage in a mortality review process looking at ‘avoidable
death’.
• Locally I review all STHK stroke deaths: any death from stroke due to known
AF without anticoagulation or have a clear reason for anticoagulation are
classed as ’avoidable’ (AMBER) as on balance of probabilities the stroke
would not occur.
• Approximately 2 deaths per month from known AF not anticoagulated in
STHK’s admitting region (will be typical of UK as a whole).

5. Economic costs of stroke in England, Wales and NI
≈80 000 people are admitted to hospital with stroke each
The total costs attributable to stroke in people with a
acute stroke per year :
NHS Social Care NHS & Social
Care
1 year £1.03 billion £633 million £1.66 billion
5 year £1.40 billion £2.05 billion £3.45 billion
NHS: Hospital admission, rehabilitation, GP attendances, prescribing
Social Care: Care home admission, packages of care (Note issues of
“who pays” for care)
Source: Stroke Sentinel National Audit Programme HE Slide deck

6. Economic costs of stroke in England, Wales and NI
0
10000
20000
30000
40000
50000
0 20 40 60 80 100
Age(years)
The blue
dots are
patients
with AF
Each dot
represents a
single
patient with
stroke
(n=80000)
Source: Stroke Sentinel National Audit
Programme HE Slide deck
Healthandsocialcarecost(£)

7. Innovation In Stroke Prevention
- Where are the focus areas?
• AF Management – how do we ensure patients remain on
appropriate management strategies which are consistently
reviewed on any contact with a health provider when anything
has changed which may affect their AF risk?
• AF Prevention – how do we ensure that we implement effective
detection strategies that deliver value for money?
• Expert Patients – patients can now self-detect with wearables:
are we ready and can we embrace disruptive changes to our AF
management?

8. Expert Patients
• Fitbits and Apple Watches have a 97% detection rate
for atrial fibrillation when paired with neural network
learning.
• https://jamanetwork.com/journals/jamacardiology/article-
abstract/2675364?redirect=true
• Phase III FDA Study ‘The Apple Heart Study’ looking at
FDA approval for Apple Watch as a health device for
AF detection.
• AliveCor has FDA license for their KardiaBand product,
which attaches to a slot on an Apple Watch band and
can produce an ECG tracing.
• Result: a cohort of patients who self-diagnose AF with
regulated devices and are being continuously
monitored by their own choice on their own device –
do we have a clear strategy how we will use that data
and what to do with those patients?

9. How quickly will this affect us?

10. AF Management
• Death or severe disability from a known and treatable condition
due to inappropriate or poorly reviewed management should
become increasingly acceptable.
• Join together information and systems information:
• ECGs done for whatever reason should trigger a check of
whether the patient has new or known AF.
• All healthcare contacts should trigger a review of risk factors.
• Much can be automated – CHADS2Vasc / HASBLED.
• Medication cause/effect labelling in EPMA systems would be a
big contributor to safety here:
https://www.linkedin.com/pulse/reducing-drug-errors-through-effective-information-management-hill/

11. Driving Change
• Stroke physicians stakeholder the results; but many other
services need to drive incident pickup.
• Effective PDSA cycles need continuous feedback.
• More effective use of SSNAP outcome data to monitor efficacy
of both management and case detection strategies.
• Effective reporting of current health and economic outcomes
of current strategies.
• Comparison to investment / detection / prediction.
• Better granularity of these results to pick up effectiveness of
detection / management efforts.
• Coupled with better results on screening / case identification so
that QI work can be triangulated.

12. What are our initial steps?
• Use Health Economics Tool to work out precise local costs at
monthly / annual estimates.
• Set up a detection strategy within the Trust to ensure AF cases
are screened and reviewed regardless of cause for admission.
• Consider integration of information through St Helens Cares
plans.

13. Thankyou